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This code package generates random tensors with user specified marginal means and covariances from one of two optional distributions. The first is the maximum-entropy-distribution with the specified marginal means and covariances. The second is the tensor-normal-distribution with the specified means and covariances.

gamaleldin/rand_tensor

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==================================================================== Gamaleldin F. Elsayed, Columbia University John P. Cunningham, Columbia University Copyright (C) 2017 Gamaleldin F. Elsayed and John P. Cunningham

This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.

You should have received a copy of the GNU General Public License along with this program. If not, see http://www.gnu.org/licenses/.

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==================================================================== Basic Usage Example

Follow the demo.ipynb file for an example

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==================================================================== randtensor This code package generates random tensors with user specified marginal means and covariances from one of two optional distributions. The first is the maximum-entropy-distribution with the specified marginal means and covariances. The second is the tensor-normal-distribution with the specified means and covariances.

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The BibTeX citations for the primary papers used in this project are:

@article{elsayed, title={Structure in neural population recordings: an expected byproduct of simpler phenomena?}, author={Gamaleldin Elsayed}, journal={Nature Neuroscience}, volume={}, year={} }

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This code package generates random tensors with user specified marginal means and covariances from one of two optional distributions. The first is the maximum-entropy-distribution with the specified marginal means and covariances. The second is the tensor-normal-distribution with the specified means and covariances.

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